Air compressor monitoring method and monitoring system

Abstract

The present invention provides an air compressor monitoring method and monitoring system, comprising the following steps: S1, installing a sensor at a designated location; S2, collecting sensor data at different locations; S3, analyzing and judging the data collected in S2; S4, analyzing the data measured in S3; S5, the system alarms and reminds of fault information; S6, the air compressor is shut down for maintenance; S7, the monitoring ends. The present invention provides a method and system for monitoring the first data unit and the second data unit collected at the position of the main components of the air compressor by setting a first data unit and a second data unit. When the temperature and pressure data of the first data unit are abnormal, the voice data of the second data unit is combined for analysis and judgment, thereby increasing the accuracy of the fault location judgment. In addition, by adjusting the fault location and then judging it when the voice data is normal, the air compressor damage caused by the failure of the component not being handled can be avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of air compressor monitoring, and in particular to an air compressor monitoring method. In addition, the present invention also relates to a monitoring system using the air compressor monitoring method. Background Art

[0002] With the continuous development of industrial production, the development of air compressor technology has attracted more and more attention. Under the traditional management model, the operating status of air compressors mainly relies on manual inspections to detect abnormalities. Due to the lack of effective data analysis methods, the energy efficiency of air compressors is low and energy waste is serious.

[0003] The main functions of an air compressor monitoring system include real-time data acquisition, status monitoring and alarms, remote monitoring, fault diagnosis and prediction, energy efficiency management, maintenance reminders and management, data reporting and analysis, and permissions management and auditing. Existing technologies include air compressor data acquisition systems that integrate data acquisition, transmission, processing, and analysis. These systems connect to the air compressor PLC via an industrial intelligent gateway for data collection and upload data in real time to a cloud platform or host computer via 5G / 4G / Wi-Fi / Ethernet. These functions work together to achieve comprehensive monitoring and management of the air compressor's operating status.

[0004] First, air compressor monitoring can prevent safety accidents. By monitoring the operating status of the air compressor in real time, equipment anomalies such as abnormal pressure and excessive temperature can be discovered in a timely manner, thus avoiding sudden equipment shutdowns or other problems and ensuring safe production in the enterprise.

[0005] Secondly, air compressor monitoring helps reduce maintenance costs. Through real-time monitoring and data analysis, it is possible to predict equipment maintenance needs and perform maintenance or component replacement in advance, thus avoiding equipment damage due to untimely maintenance and reducing repair costs.

[0006] Furthermore, air compressor monitoring can improve manpower utilization. Automated monitoring systems can reduce the frequency of manual inspections, lowering labor costs while also improving response times to equipment failures and ensuring continuous production line operation.

[0007] In practical applications, a major challenge facing air compressor monitoring systems is the lack of timely and accurate data support, which hinders energy consumption analysis and control. Existing monitoring systems collect and monitor equipment status, operating parameters, and energy consumption data. These systems compare and analyze this data against preset thresholds, enabling them to respond with remote adjustments, fault warnings, and manual maintenance.

[0008] The data collected for operating parameters mainly includes host temperature, exhaust pressure, instantaneous flow rate, cumulative flow rate, operating status, operating time, etc. The main collection method is to use sensors to collect data, convert the collected information into digital signals, and then organize and analyze the collected data in the data center or host computer to quickly locate the problem, diagnose it, and take appropriate measures.

[0009] In reality, when a monitoring system identifies abnormal data, it automatically issues an alarm and shuts down the compressor pending repairs. However, because the air compressor's motion data is variable, abnormal temperature data caused by factors such as poor terminal contact and sensor failure, rather than problems with air compressor components, can lead to misjudgment, resulting in compressor downtime and unnecessary production interruption. Summary of the Invention

[0010] In view of this, the present invention aims to propose an air compressor monitoring method to obtain accurate collected data while avoiding air compressor shutdown due to system misjudgment.

[0011] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0012] An air compressor monitoring method comprises the following steps:

[0013] S1, install the sensor at the designated location;

[0014] S2, collecting sensor data at different locations, the sensor data including a first data unit and a second data unit, the first data unit including a first temperature value and a first pressure value, and the second data unit including voice data;

[0015] S3, analyzes and judges the data collected in S2;

[0016] S31, analyzing the first data unit in S2 to determine whether the measured first temperature value or first pressure value is abnormal;

[0017] S32, processing and analyzing the second data unit in S2, and determining whether the voice data is abnormal;

[0018] S4, analyzing the first temperature value, the first pressure value, and the voice data measured in S3;

[0019] The analysis steps are as follows:

[0020] S41, when one of the first temperature value and the first pressure value measured in S31 and S32 is abnormal, and the voice data is abnormal, execute S5;

[0021] S42, when the first temperature value or the first pressure value obtained in S31 is abnormal and the voice data is normal, adjust the fault location and then make a judgment. If the data is abnormal, execute S5;

[0022] S5, the system alarms and prompts fault information, and manually checks whether the sensor of the first data unit is invalid. If it is not invalid, execute S6;

[0023] S6, the air compressor is shut down for maintenance;

[0024] S7, end monitoring.

[0025] Furthermore, S42 includes the following steps:

[0026] S421, identifying abnormal data of the first temperature value and the first pressure value, locking the abnormal part, and making corresponding adjustments;

[0027] S422, collecting again the second pressure value and the second temperature value of the location of the abnormal data in S421;

[0028] S423, determining whether the measured second pressure value and second temperature value are abnormal. If the measured data are normal, no processing is performed. If the measured data are abnormal, executing S5.

[0029] Furthermore, the installation locations of the sensors in S1 include the intake pipe, the exhaust pipe, the power system, the cylinder block, and the cooling system.

[0030] Furthermore, S32 includes the following steps:

[0031] S321, collecting voice data at the sensor installation location described in step 3;

[0032] S322, filtering the voice data collected in S321 using a filtering algorithm;

[0033] S323, performing equipment failure predictive analysis based on the filtered voice data to obtain prediction results

[0034] S324, set the prediction result threshold and judge the prediction structure obtained in S323 The result is compared with the threshold value, and if it exceeds the numerical range of the threshold value, it is determined to be abnormal.

[0035] Furthermore, in S322, the filtering method adopts a nanofiltration algorithm, and the calculation formula is as follows:

[0036]

[0037] in, n(t) and y(t) are the original speech signal, noise, and noise-contaminated speech data of the i-th frame respectively;

[0038] s(t)=y(t)*H(n);

[0039] Among them, H(n) is a linear filter and s(t) is the pure original speech signal.

[0040] Furthermore, in S323, the equipment failure predictive analysis includes the following steps:

[0041] S3231, calculate the air compression ratio of the air compressor,

[0042] That is (v1 / v2) (γ-1) ;

[0043] Where v1 is the inlet air volume, V2 is the outlet air volume, and γ is the adiabatic index of the air;

[0044] S3232, by measuring the inlet and outlet pressures of the tested component and combining them with the air compression ratio, determine whether there is a significant difference in pressure conversion using the following formula;

[0045] (v1 / v2) (γ-1) p2-p1;

[0046] Where p1 is the inlet pressure and p2 is the outlet pressure;

[0047] S3233: The soundprint decibel d2 collected by the acquisition sensor at time T and the standard soundprint decibel d1 are compared and analyzed, and the estimated value after linear filtering is comprehensively analyzed to obtain the fault prediction model:

[0048]

[0049] represents the actual value of the prediction result at time t, and s(t) is the pure original speech signal calculated in step S322.

[0050] Furthermore, in S324, when the actual value of the prediction result When the threshold value is not exceeded, a prediction analysis curve is drawn and maintenance conditions are set;

[0051] When the actual value of the predicted result If the threshold is exceeded three times, it is determined to be abnormal.

[0052] Compared with the prior art, the present invention has the following advantages:

[0053] The air compressor monitoring method disclosed herein utilizes a first data unit and a second data unit to monitor the positions of key air compressor components. When the temperature and pressure data from the first data unit are abnormal, the second data unit's voice data is combined for analysis and judgment, thereby increasing the accuracy of fault location determination. Furthermore, by adjusting the fault location and then re-determining it when the voice data is normal, air compressor damage caused by untreated component failures can be avoided.

[0054] In addition, the air compressor monitoring method of the present invention combines the superposition judgment of abnormal data in the traditional monitoring system with the sound collection in actual applications to improve the judgment accuracy. And when there is no abnormality in the voice data and there are abnormalities in the temperature and pressure, the location where the abnormal data occurs is adjusted. If the data is abnormal after adjustment, it means that there is a hidden fault in this location, and it can be checked on weekends. If the data is still abnormal after adjustment, the system alarm is taken, and the false stop is eliminated by manually checking the sensor. This method effectively avoids production stoppages caused by misjudgment of the monitoring system and ensures the operational stability of the air compressor.

[0055] Another object of the present invention is to provide an air compressor monitoring system, which uses the air compressor monitoring method described above to monitor the air compressor, and the system includes:

[0056] Controller, data receiver, sensor, display module and alarm;

[0057] The output end of the data receiving end, the display module and the alarm are respectively connected to the controller; the controller is used to control the operation or stop of the air compressor, and adjust various working modes and parameters of the air compressor.

[0058] Furthermore, the sensors include pressure sensors, temperature sensors, sound collectors, flow sensors, vibration sensors, etc.

[0059] Furthermore, the sound collector includes a mounting frame, a sound collection mainboard arranged on the mounting frame, and a noise-proof component.

[0060] The air compressor monitoring system described in the present invention adopts the air compressor monitoring method as described above. Before the air compressor needs to be shut down due to data abnormalities, a judgment step of voiceprint collection is added. When the temperature and pressure data are abnormal, the voiceprint is judged, thereby further improving the accuracy of discovering the fault location and avoiding the phenomenon of the air compressor being forced to stop accidentally due to data abnormalities caused by faults such as temperature and pressure sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The accompanying drawings, which constitute part of the present invention, are provided to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are provided to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0062] Figure 1 This is a flow chart of the air compressor monitoring method according to an embodiment of the present invention;

[0063] Figure 2 As described in the embodiments of the present invention;

[0064] Figure 3 The figure is a cross-sectional schematic diagram of the sound collector according to an embodiment of the present invention.

[0065] Description of reference numerals:

[0066] 1. Mounting frame; 2. Collection mainboard; 3. Windproof cotton; 4. PEEK film; 5. Connecting plate; 6. Sealing ring;

[0067] 101. Upper cover; 102. Lower cover. DETAILED DESCRIPTION

[0068] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0069] In the description of the present invention, it should be noted that the terms "upper," "lower," "inner," and "back" and other terms indicating orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0070] Furthermore, in the description of the present invention, unless otherwise expressly defined, the terms "mounted," "connected," "connect," and "connector" should be interpreted broadly. For example, these terms may refer to fixed, removable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediary; or internal communication between two components. Those skilled in the art will appreciate the specific meanings of these terms in the present invention based on the specific circumstances.

[0071] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0072] This embodiment relates to an air compressor monitoring method, which includes the following steps.

[0073] S1. Install sensors at designated locations. In this embodiment, these locations include the intake and exhaust lines, the powertrain, the cylinder block, and the cooling system. Common air compressor faults, such as excessively high exhaust temperature, compressor stalls, low exhaust pressure, and abnormal compressor noise, can all be monitored using temperature, pressure, and sound. Therefore, temperature sensors, pressure sensors, and sound collectors are installed at these locations.

[0074] The pressure value described in this embodiment is the air pressure value. Of course, the voltage, current and other data also need to be monitored in the monitoring system. The monitoring situation of the electrical part is the same as that of the prior art and is not limited here.

[0075] In step S2 , sensor data at different locations needs to be collected. The sensor data includes a first data unit and a second data unit. The first data unit includes a first temperature value and a first pressure value, and the second data unit includes voice data.

[0076] In step S3, the data collected in S2 is analyzed and judged;

[0077] S31: Analyze the first data unit in S2 to determine whether the measured first temperature value or first pressure value is abnormal. Specifically, temperature thresholds and pressure thresholds are set for different components in the system. When the measured first temperature value or first pressure value exceeds the corresponding threshold, it is considered a data abnormality.

[0078] S32, processing and analyzing the second data unit in S2, and determining whether the voice data is abnormal.

[0079] S32 includes the following steps:

[0080] S321, collecting voice data from the sensor installation location in step 3;

[0081] S322, filtering the voice data collected in S321 using a filtering algorithm;

[0082] In S322, the filtering method used is the nanofiltration algorithm, and the calculation formula is as follows:

[0083]

[0084] in, n(t) and y(t) are the original speech signal, noise, and noise-contaminated speech data of the i-th frame respectively;

[0085] s(t)=y(t)*H(n);

[0086] Among them, H(n) is a linear filter and s(t) is the pure original speech signal.

[0087] Specifically, the acquisition sensor includes a sound collector, a temperature sensor, a pressure sensor, etc. The speech data collected by the acquisition sensor is a noisy speech signal y(t), which is obtained from the noise n(t) and the original speech signal of the i-th frame. The estimated value s(t) obtained by filtering with a linear filter is the pure original speech signal separated after filtering. This ensures that the speech data is more accurate and reduces the probability of misjudgment.

[0088] As shown in the table below, in this embodiment, given speech data collection at five different detection positions, H(n) is approximately 0.6. After estimation, the following table shows the pure original speech signals at different time points:

[0089] Time step y(t) s(t) 1 0.6 0.36 2 0.5 0.3 3 0.4 0.24 4 0.3 0.18 5 0.2 0.12

[0090] S323, performing equipment failure predictive analysis based on the filtered voice data to obtain prediction results

[0091] In S323, the equipment failure predictive analysis includes the following steps:

[0092] S3231, calculate the air compression ratio of the air compressor,

[0093] That is (v1 / v2) (γ-1) ;

[0094] Where v1 is the inlet air volume, V2 is the outlet air volume, and γ is the adiabatic index of the air;

[0095] S3232, by measuring the inlet and outlet pressures of the tested component and combining them with the air compression ratio, determine whether there is a significant difference in pressure conversion using the following formula;

[0096] (v1 / v2) (γ-1) p2-p1;

[0097] Where p1 is the inlet pressure and p2 is the outlet pressure;

[0098] It should be noted that the above-mentioned p1 and p2 are the inlet and outlet pressures of the above-mentioned intake pipe, exhaust pipe, power system, cylinder block, and cooling system.

[0099] S3233 compares and analyzes the soundprint decibel d2 collected by the sensor at time T with the standard soundprint decibel d1. A comprehensive analysis is performed on the estimated value obtained after linear filtering to derive a fault prediction model:

[0100]

[0101] represents the actual value of the prediction result at time t, and s(t) is the pure original speech signal calculated in step S322.

[0102] S324, set the prediction result threshold and judge the prediction structure obtained in S323 The value is compared with the threshold and is considered abnormal if it exceeds the threshold value range.

[0103] In S324, when the actual value of the prediction result When the threshold value is not exceeded, a prediction analysis curve is drawn and maintenance conditions are set;

[0104] When the actual value of the predicted result If the threshold is exceeded three times, it is considered abnormal.

[0105] In the specific process, Figure 2 As shown in the figure, the data of a certain air compressor power system is measured, v1=1, v2=1.8, p2=3.2, p1=1.0, d1=20, d2=21.6, and the equipment operation stability threshold is set to ±1.5 according to the characteristics of the air compressor equipment. By collecting and analyzing the soundprints of the suction pipe, exhaust pipe, power system, cylinder block, and cooling system over a period of time, the prediction formula is used to obtain the following: Figure 2 The analytical curve is shown.

[0106] from Figure 2 The analysis curve shows that The operation is in a stable and steady state. At around 28:00 The value suddenly increases. If it exceeds 1.5, it means that there is a problem with the power system.

[0107] In addition, it is also possible to Further analysis is performed to obtain maintenance conditions, which are set based on daily failure experience of air compressor equipment, such as ±1, ±0.9, ±0.8, ±0.7, ±0.6, ±0.5, etc.

[0108] Furthermore, the equipment optimization strategy can be carried out according to the forecast analysis curve. When it is within the threshold range, the actual value of the prediction result obtained in step S3 of the device is The equipment maintenance methods are formulated according to the changes in the equipment. The equipment maintenance methods are set according to the use area, number of uses, use environment and common faults of the air compressor, which will not be detailed here.

[0109] In step S4, the first temperature value, the first pressure value, and the voice data measured in step S3 are analyzed.

[0110] The analysis steps are as follows:

[0111] S41, when one of the first temperature value and the first pressure value measured in S31 and S32 is abnormal, and the voice data is abnormal, execute S5;

[0112] S42, when the first temperature value or the first pressure value obtained in S31 is abnormal and the voice data is normal, adjust the fault location and then make a judgment. If the data is abnormal, execute S5;

[0113] Wherein, S42 includes the following steps:

[0114] S421 identifies abnormal data in the first temperature and first pressure values, identifies the abnormal location, and makes appropriate adjustments. Specifically, these adjustments include pressure and temperature adjustments. Pressure adjustment occurs when the air compressor pressure is found to be too high or too low. This can be done by adjusting the pressure switch or pressure reducing valve. When making adjustments, the pressure should be increased or decreased gradually to avoid damage to the equipment caused by excessive pressure fluctuations.

[0115] If the temperature of the above components is found to be too high during temperature adjustment, consider adding heat dissipation equipment or improving ventilation conditions, and at the same time reasonably distribute the workload of the equipment.

[0116] S422, collecting again the second pressure value and the second temperature value of the location of the abnormal data in S421;

[0117] S423, determining whether the measured second pressure value and second temperature value are abnormal. If the measured data are normal, no processing is performed. If the measured data are abnormal, executing S5.

[0118] S5, the system alarms and prompts fault information, and manually checks whether the sensor of the first data unit is invalid. If it is not invalid, execute S6;

[0119] S6, the air compressor is shut down for maintenance;

[0120] S7, end monitoring.

[0121] The air compressor monitoring method disclosed herein utilizes a first data unit and a second data unit to monitor the positions of key air compressor components. When the temperature and pressure data from the first data unit are abnormal, the second data unit's voice data is combined for analysis and judgment, thereby increasing the accuracy of fault location determination. Furthermore, by adjusting the fault location and then re-determining it when the voice data is normal, air compressor damage caused by untreated component failures can be avoided.

[0122] In addition, the air compressor monitoring method of the present invention combines the superposition judgment of abnormal data in the traditional monitoring system with the sound collection in actual applications to improve the judgment accuracy. And when there is no abnormality in the voice data and there are abnormalities in the temperature and pressure, the location where the abnormal data occurs is adjusted. If the data is abnormal after adjustment, it means that there is a hidden fault in this location, and it can be checked on weekends. If the data is still abnormal after adjustment, the system alarm is taken, and the false stop is eliminated by manually checking the sensor. This method effectively avoids production stoppages caused by misjudgment of the monitoring system and ensures the operational stability of the air compressor.

[0123] This embodiment also relates to an air compressor monitoring system, which uses the above-mentioned air compressor monitoring method to monitor the air compressor. The system includes:

[0124] Controller, data receiver, sensor, display module and alarm;

[0125] The output end of the data receiving end, the display module and the alarm are respectively connected to the controller; the controller is used to control the operation or stop of the air compressor and adjust various working modes and parameters of the air compressor.

[0126] The controller is a PLC controller, preferably a Mitsubishi programmable controller, an AB programmable controller, or a Siemens programmable controller. Furthermore, a wireless transmitter is provided to wirelessly connect to a wireless receiver, which is connected to a monitoring terminal to notify maintenance personnel or management personnel of the monitoring results, thereby achieving a closed-loop monitoring system.

[0127] In addition, sensors include pressure sensors, temperature sensors, sound collectors, flow sensors, vibration sensors, etc.

[0128] Specifically, the temperature sensor of this embodiment uses a thermocouple or thermistor. The pressure sensor uses a pressure sensor and a differential pressure sensor. In addition, a liquid level sensor is provided: it is used to measure the liquid level of the lubricating oil or cooling water of the air compressor. Common types include float level sensors and capacitance level sensors. A flow sensor is used to measure the gas flow of the air compressor. Common types include turbine flow sensors and differential pressure flow sensors. A gas concentration sensor is used to measure the gas concentration in the exhaust gas of the air compressor. Common types include oxygen sensors and carbon dioxide sensors.

[0129] In addition, a vibration sensor is also provided to monitor the vibration of the air compressor to detect faults in a timely manner. A lubricating oil sensor is used to monitor the quality and usage of the air compressor lubricating oil for timely replacement or maintenance. A moisture sensor is used to monitor the moisture content in the air compressor exhaust to remove moisture in a timely manner.

[0130] Furthermore, if Figure 3As shown, the sound collector includes a mounting frame 1, a sound collection mainboard 2 mounted on the mounting frame 1, and a noise-canceling component. The sound collection mainboard 2 is a MIC mainboard. The mounting frame 1 includes a storage space within which the sound collection mainboard 2 is mounted. Windshield 3 is located on the outside of the sound collection mainboard 2, and a PEEK membrane 4 is located on the outside of the windshield 3. The PEEK membrane 4 filters external dust and oil, ensuring the sound collection performance of the sound collector. The windshield 3 reduces noise, further improving the accuracy of sound collection.

[0131] In addition, if Figure 3 As shown, to prevent damage to the peek film, a metal mesh is provided on the outside of the peek film. To facilitate assembly, the mounting frame 1 includes an upper cover 101 and a lower cover 102. To reduce the impact of noise on the MIC motherboard, a sealing ring 6 is provided between the upper cover 101 and the lower cover 102 to prevent external wind noise from entering the vicinity of the MIC motherboard.

[0132] In addition, if Figure 3 As shown, the mounting bracket 1 secures the sound collector to the corresponding mounting location using bolts and a connecting plate 5. For example, the mounting bracket 1 and connecting plate 5 secure the sound collector to the mounting bolts of the power system engine. Alternatively, the soundprint collector can be mounted on the intake and exhaust ducts using straps, and the temperature and pressure sensors can be located within the ducts.

[0133] The air compressor monitoring system described in the present invention adopts the air compressor monitoring method as described above. Before the air compressor needs to be shut down due to data abnormalities, a judgment step of voiceprint collection is added. When the temperature and pressure data are abnormal, the voiceprint is judged, thereby further improving the accuracy of discovering the fault location and avoiding the phenomenon of the air compressor being forced to stop accidentally due to data abnormalities caused by faults such as temperature and pressure sensors.

[0134] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for monitoring an air compressor, characterized in that: The following steps are included: S1, install the sensor at the designated location; S2, collecting sensor data at different locations, the sensor data including a first data unit and a second data unit, the first data unit including a first temperature value and a first pressure value, and the second data unit including voice data; S3, analyzes and judges the data collected in S2; S31, analyzing the first data unit in S2 to determine whether the measured first temperature value or first pressure value is abnormal; S32, processing and analyzing the second data unit in S2, and determining whether the voice data is abnormal; S4, analyzing the first temperature value, the first pressure value, and the voice data measured in S3; The analysis steps are as follows: S41, when one of the first temperature value and the first pressure value measured in S31 and S32 is abnormal, and the voice data is abnormal, execute S5; S42, when the first temperature value or the first pressure value obtained in S31 is abnormal and the voice data is normal, adjust the fault location and then make a judgment. If the data is abnormal, execute S5; S5, the system alarms and prompts fault information, and manually checks whether the sensor of the first data unit is invalid. If it is not invalid, execute S6; S6, the air compressor is shut down for maintenance; S7, end monitoring; S42 includes the following steps: S421, identifying abnormal data of the first temperature value and the first pressure value, identifying the abnormal location, and performing corresponding adjustments; the adjustments include pressure adjustment and temperature adjustment. The pressure adjustment is performed by adjusting a pressure switch or a pressure reducing valve when the air compressor pressure is found to be too high or too low; The temperature adjustment is to add heat dissipation equipment or improve ventilation conditions when the temperature of the air compressor is found to be too high, and to reasonably distribute the workload of the equipment; S422, collecting again the second pressure value and the second temperature value of the location of the abnormal data in S421; S423, determining whether the measured second pressure value and second temperature value are abnormal. If the measured data are normal, no processing is performed. If the measured data are abnormal, executing S5.

2. The air compressor monitoring method according to claim 1, characterized in that: The installation locations of the sensors in S1 include the intake pipe, exhaust pipe, power system, cylinder block, and cooling system.

3. The air compressor monitoring method according to claim 2, characterized in that: S32 includes the following steps: S321, collecting voice data at the sensor installation location described in step 3; S322, filtering the voice data collected in S321 using a filtering algorithm; S323, performing equipment failure predictive analysis based on the filtered voice data to obtain a prediction result; S324, setting a prediction result threshold, comparing the prediction result obtained in S323 with the threshold, and determining it as abnormal if it exceeds the numerical range of the threshold.

4. The air compressor monitoring method according to claim 3, characterized in that: In S322, the filtering method adopts the nanofiltration algorithm, and the calculation formula is as follows: ； in, (t), n(t) and y(t) are the original speech signal, noise and noise-contaminated speech data of the i-th frame respectively; ； Among them, H(n) is a linear filter and s(t) is the pure original speech signal.

5. The air compressor monitoring method according to claim 3, characterized in that: In S324, when the actual value of the prediction result does not exceed the range of the threshold, a prediction analysis curve is drawn and maintenance conditions are set; When the actual value of the prediction result exceeds the threshold value three times, it is determined to be abnormal.

6. An air compressor monitoring system, characterized in that: An air compressor monitoring method according to any one of claims 1 to 5 is used to monitor an air compressor, the system comprising: Controller, data receiver, sensor, display module and alarm; The output end of the data receiving end, the display module and the alarm are respectively connected to the controller; the controller is used to control the operation or stop of the air compressor, and adjust various working modes and parameters of the air compressor.

7. The air compressor monitoring system according to claim 6, characterized in that: The sensors include a pressure sensor, a temperature sensor, a sound collector, a flow sensor, and a vibration sensor.

8. The air compressor monitoring system according to claim 7, characterized in that: The sound collector comprises a mounting frame (1), a sound collection mainboard (2) arranged on the mounting frame (1), and a noise-proof component.

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